Faculty Bibliography

Adaptation to a moving visual pattern induces shifts in the perceived motion of subsequently viewed moving patterns. Explanations of such effects are typically based on adaptation-induced sensitivity changes in spatio-temporal frequency tuned mechanisms (STFMs). An alternative hypothesis is that adaptation occurs in mechanisms that independently encode direction and speed (DSMs). Yet a third possibility is that adaptation occurs in mechanisms that encode 2D pattern velocity (VMs). We performed a series of psychophysical experiments to examine predictions made by each of the three hypotheses. The results indicate that: (1) adaptation-induced shifts are relatively independent of spatial pattern of both adapting and test stimuli; (2) the shift in perceived direction of motion of a plaid stimulus after adaptation to a grating indicates a shift in the motion of the plaid pattern, and not a shift in the motion of the plaid components; and (3) the 2D pattern of shift in perceived velocity radiates away from the adaptation velocity, and is inseparable in speed and direction of motion. Taken together, these results are most consistent with the VM adaptation hypothesis

Electrophysiological studies indicate that neurons in the middle temporal (MT) area of the primate brain are selective for the velocity of visual stimuli. This paper describes a computational model of MT physiology, in which local image velocities are represented via the distribution of MT neuronal responses. The computation is performed in two stages, corresponding to neurons in cortical areas V1 and MT. Each stage computes a weighted linear sum of inputs, followed by rectification and divisive normalization. V1 receptive field weights are designed for orientation and direction selectivity. MT receptive field weights are designed for velocity (both speed and direction) selectivity. The paper includes computational simulations accounting for a wide range of physiological data, and describes experiments that could be used to further test and refine the model

We describe a novel formulation of the range recovery problem based on computation of the differential variation in image intensities with respect to changes in camera position. This method uses a single stationary camera and a pair of calibrated optical masks to measure this differential quantity directly. We also describe a variant based on changes in aperture size. The subsequent computation of the range image involves simple arithmetic operations and is suitable for real-time implementation. We present the theory of this technique and show results from a prototype camera that we have constructed